长春工程学院学报（自然科学版）2024，Vol.25Issue(1)：85-89,5.DOI:10.3969/j.issn.1009-8984.2024.01.016
基于卷积神经网络的图像分割方法研究
Research on Image Segmentation Methods Based on Convolutional Neural Networks
摘要
Abstract
Aiming at the problems of multiple parameters,overfitting leading to low image segmentation ac-curacy and low algorithm efficiency in traditional convolutional neural networks,maximum pooling pro-cessing is adopted to replace the downsampling layer,and an improved CNN structure is constructed to ob-tain the U-Net convolutional neural network,which is further improved.The improved U-Net convolution-al neural network is applied to high-resolution remote sensing images,and the results show that it can per-form fine and complete segmentation of small buildings in remote sensing images.In addition,by compa-ring with FCN32s,SegNet,and FCN8s,it is pointed out that the improved U-Net convolutional neural net-work has better performance in remote sensing image segmentation.关键词
卷积神经网络/图像分割/遥感图像Key words
convolutional neural network/image segmentation/remote sensing image分类
信息技术与安全科学引用本文复制引用
戚伟,葛斌,桑冬青..基于卷积神经网络的图像分割方法研究[J].长春工程学院学报（自然科学版）,2024,25(1):85-89,5.基金项目
安徽省高等学校自然科学研究重点项目(KJ2020A1163) （KJ2020A1163）
